Pre-Train BERT from scratch: Solution for Company Domain Knowledge Data | PyTorch (SBERT 51)
When it comes to training BERT models for specific domain knowledge, it can be a challenging task. However, with the right tools and techniques, it is possible to train your own BERT model from scratch. In this article, we will explore how to pre-train BERT from scratch using PyTorch and SBERT 51.
What is BERT?
BERT (Bidirectional Encoder Representations from Transformers) is a state-of-the-art natural language processing model developed by Google. It is designed to understand context and meaning in natural language text, making it ideal for a wide range of NLP tasks.
Why Pre-Train BERT from Scratch?
Pre-training BERT from scratch allows you to fine-tune the model for your specific domain knowledge. By training the model on your own data, you can improve its performance on tasks related to your company’s domain.
Using PyTorch and SBERT 51
PyTorch is a popular deep learning framework that is commonly used for training neural network models. SBERT 51 is a pre-trained BERT model that has been fine-tuned on the SBERT dataset, making it suitable for domain-specific knowledge tasks.
How to Pre-Train BERT from Scratch
To pre-train BERT from scratch using PyTorch and SBERT 51, you will need to follow these steps:
- Prepare your training data: Gather a large dataset of text data from your company’s domain.
- Tokenize your data: Use a tokenizer to split your text data into tokens that BERT can understand.
- Pre-train your model: Use PyTorch and SBERT 51 to pre-train your BERT model on your domain-specific data.
- Fine-tune your model: After pre-training, fine-tune your model on specific tasks related to your company’s domain.
Conclusion
Training BERT models from scratch can be a powerful solution for company domain knowledge data. By using PyTorch and SBERT 51, you can customize BERT for your specific domain and achieve better performance on NLP tasks. Remember to carefully prepare your training data and follow best practices for pre-training and fine-tuning your model.
Can i use this model for sentimental analysis and text summarisation after fine tuning this mlm bert model ??
Hello friend. Firstly, congratulations on the video. Beautiful! For datasets in English it works perfectly, however, I tried to implement it for Brazilian Portuguese and the Validation Loss metric always returns NaN. Any tips on what could be causing this? Thanks!
This model trained can it be used for next word prediction. Also following this process can I trained other languages like this
This model trained can it be used for next word prediction. Also following this process can I trained other languages like this
Your version of the Colab is so Different from the one in the Description or the tutorial.. Can you share it?
Someone could make an AI to watch a video like this one, and using AI and OCR together, Piece together the file you scroll through in the video to make a Text file of the code.
Right now, this is a long an tedious task. the AI would have to: detect the line numbers, screenshot the video, watch for when you scroll down, screenshot again, OCR only the New lines or ignore duplicate lines from the OCR, ignore when you switch to viewing something else, oh My, it gets complicated.
You should code that… It would be a big Hit.
Hello, I am trying to implement the same with llama2. But for training purpose i need to modify the llama2 model config. is that possible?
How do i create a dataset for domain adaptation. My usecase is very specific and there's nothing about it on the internet but i do have a really long file with just words related to the domain. How do i move from there? thanks
please share notebooks
Thanks for your time and effort in putting this video together. It is very informative. Did you pad the text in your own dataset before training the tokenizer? Or was the input text from the dataset all variable length?
Could you show how you load the model correctly as SentenceBert model? I have used the approach that you show in the video and then load the trained model in the SentenceTransformer constructor but I get a bunge of errors.
What techniques do you recommend to improve the loss? Change the size of the vocabulary, num of epochs? Would it make sense to adjust the vocab_size to the number of unique tokens in the corpus?
I don't quite understand where the difference is between this approach an directly fine-tuning a SBERT model? Is it that SBERT uses a Simaese network of two Bert models and we just plugin our trained Bert models into the SBER Siamese network? Why would you prefer this method over fine-tuning a SBERT model directly?
This is great! Which video shows the "three lines of code" for training of a custom SBERT model?
OMG! You're amazing!!! I struggle with Colab. Total noob but I'm so excited about AI and so I'm burning my brain trying to dive in! This is fantastic.
I am a beginner In this stuff, but I learn a lot in this channel. Hopefully more from this kind of tutor. Many thanks
The GOAT. Uni students who have to make their own llm model as projects will be referencing this.
This is the fifth video I'm watching today and not a single time there's nothing missed run cell! …. beautiful!
Nice
I use all of this code identically, except i upload my own personal csv file with one text sequence on each line. Everything works fine until i train, it says "RuntimeError: Index put requires the source and destination dtypes match, got Float for the destination and Long for the source."
this perplexes me because it is text data just like yours or the cc_news dataset. Is there anyway I can change the datasets source values to Float? or the destination to Long?
Just what I was looking for! Your channel is a goldmine. Thanks so much for making these enlightening videos, I'll be going through all of them 🤯 cheers from Argentina